[HTML][HTML] A review of supervised object-based land-cover image classification
L Ma, M Li, X Ma, L Cheng, P Du, Y Liu - ISPRS Journal of Photogrammetry …, 2017 - Elsevier
Object-based image classification for land-cover map** purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …
imagery has attracted significant attention in recent years. Numerous studies conducted over …
Advances of four machine learning methods for spatial data handling: A review
Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …
Regression and classification models are normally used to extract useful geographic …
Deep feature enhancement method for land cover with irregular and sparse spatial distribution features: A case study on open-pit mining
Land cover classification in mining areas (LCMA) is essential for the environmental
assessment of mines and plays a crucial role in their sustainable development. The shapes …
assessment of mines and plays a crucial role in their sustainable development. The shapes …
Active multi-kernel domain adaptation for hyperspectral image classification
Recent years have witnessed the quick progress of the hyperspectral images (HSI)
classification. Most of existing studies either heavily rely on the expensive label information …
classification. Most of existing studies either heavily rely on the expensive label information …
An active deep learning approach for minimally supervised PolSAR image classification
Recently, deep neural networks have received intense interests in polarimetric synthetic
aperture radar (PolSAR) image classification. However, its success is subject to the …
aperture radar (PolSAR) image classification. However, its success is subject to the …
A benchmark and comparison of active learning for logistic regression
Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …
Meta-XGBoost for hyperspectral image classification using extended MSER-guided morphological profiles
To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing
image classification tasks, XGBoost was first introduced and comparatively investigated for …
image classification tasks, XGBoost was first introduced and comparatively investigated for …
Unsupervised feature extraction in hyperspectral images based on Wasserstein generative adversarial network
Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing.
Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral …
Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral …
A review of fine-scale land use and land cover classification in open-pit mining areas by remote sensing techniques
Over recent decades, fine-scale land use and land cover classification in open-pit mine
areas (LCCMA) has become very important for understanding the influence of mining …
areas (LCCMA) has become very important for understanding the influence of mining …
Multiview spatial–spectral active learning for hyperspectral image classification
Supervised classification algorithms on the intricate ground object information of
hyperspectral images (HSIs) require a large number of training samples that are annotated …
hyperspectral images (HSIs) require a large number of training samples that are annotated …